I. Technical Field
The present invention relates generally to computer modeling and management of systems and, more particularly, to computer simulation techniques with real-time system monitoring and prediction of electrical system performance.
II. Background
Electric generation has traditionally been performed by large-scale centralized facilities that are powered by fossil fuels or nuclear power or hydropower. These systems were monolithic and unidirectional systems. Smart grid technology has evolved to allow multidirectional communications and transmission of power. The smart grid increases the connectivity between power generation companies and power distributors. The smart grid also provides digital two-way communications with end users. For example, consumers can have smart meters installed at their premises that can report energy usages patterns to the utility for monitoring and billing purposes. Consumers may also have smart devices installed in their homes that can receive information from energy providers over the smart grid that inform the devices when demand for electricity is high causing the cost of electricity to rise. Some devices may reduce electricity usage during peak periods or defer usage until demand decreases. For example, an electric hot water heater may defer heating water in its tank until low demand periods when the cost of electricity may be significantly lower that during peak demand periods. In another example, non-essential devices may be shut down during peak demand periods to reduce load on the system. For example, in some areas, consumers can contract to allow an electric utility company to switch off their air conditioner systems for short periods of time during peak demand periods.
Distributed electrical power generation has also evolved and has become another aspect of the smart grid. Distributed generation systems include smaller-scale power generation facilities that can be used in addition to or instead of the traditional centralized facilities. Microgrids
A microgrid is a localized grouping of electrical resources and loads that are typically connected to and synchronized with the traditional centralized electrical grid (also referred to herein as the macrogrid). A microgrid is typically connected to the macrogrid at a single point of connection, and the microgrid can typically disconnect from the macrogrid and function as an autonomous power system. The microgrid typically includes control independent of the macrogrid that allows the microgrid to be adjusted for changes in operating parameters, such as local load levels, independently of the macrogrid. Microgrids can be used as part of a distributed energy system where energy is generation is decentralized and energy is generated from many small sources. For example, a microgrid may be a smaller generation station that is designed to supply power to a single building or set of buildings, such as a hospital or office building complex. A microgrid might also be designed to power a larger area, such as a university campus or industrial complex that includes a larger number of buildings and can include greater load. Depending upon the specific implementation, the microgrid can have varying reliability requirements. For example, an implementation of a microgrid at a hospital or an industrial complex may have greater reliability requirements than a microgrid supplying power to a residential dormitories and classrooms on a university campus.
Microgrids can provide a hybrid power infrastructure where power from the conventional macrogrid is used in combination with the power generated onsite by the microgrid. Electrical power is often sold on complex market, and distributed energy systems, such as microgrids, add additional complexity to the market. Microgrids can sell excess power to the macrogrid and can purchase power from the macrogrid in order to meet local demand in excess of the generation capacity of the microgrid.
Optimization of market-based power systems is a critical component of distributed energy generation management. Demand for electricity and market conditions, such as pricing and availability of electrical power, create a complex market, and consideration must be taken for overall availability and reliability of the system. Various scenarios under consideration can impact or be impacted by external events, such as routine maintenance, system changes, or unplanned events that impact the electrical power network. Conventional approaches to market-based optimization do not take into account these potential effects on the power market.
Conventional systems provide market-based pricing of distributed energy off-line and do not consider real-time power network conditions. Conventional systems also do not provide for real-time evaluation of microgrid data to generated predicted impacts on availability and reliability of the microgrids.
Computer models of complex systems, such as microgrids, enable improved system design, development, and implementation through techniques for off-line simulation of the system operation. That is, system models can be created that computers can “operate” in a virtual environment to determine design parameters. All manner of systems can be modeled, designed, and virtually operated in this way, including machinery, factories, electrical power and distribution systems, processing plants, devices, chemical processes, biological systems, and the like. Such simulation techniques have resulted in reduced development costs and superior operation.
Design and production processes have benefited greatly from such computer simulation techniques, and such techniques are relatively well developed, but such techniques have not been applied in real-time, e.g., for real-time operational monitoring and management. In addition, predictive failure analysis techniques do not generally use real-time data that reflect actual system operation. Greater efforts at real-time operational monitoring and management would provide more accurate and timely suggestions for operational decisions, and such techniques applied to failure analysis would provide improved predictions of system problems before they occur. With such improved techniques, operational costs could be greatly reduced.
For example, mission critical electrical systems, e.g., for data centers or nuclear power facilities, must be designed to ensure that power is always available. Thus, the systems must be as failure proof as possible, and many layers of redundancy must be designed in to ensure that there is always a backup in case of a failure. It will be understood that such systems are highly complex, a complexity made even greater as a result of the required redundancy. Computer design and modeling programs allow for the design of such systems by allowing a designer to model the system and simulate its operation. Thus, the designer can ensure that the system will operate as intended before the facility is constructed.
Once the facility is constructed, however, the design is typically only referred to when there is a failure. In other words, once there is failure, the system design is used to trace the failure and take corrective action; however, because such design are complex, and there are many interdependencies, it can be extremely difficult and time consuming to track the failure and all its dependencies and then take corrective action that does not result in other system disturbances.
Conventional system modeling and analytics solutions typically require a vendor-specific data collection engine that is proprietary and incompatible with competing technologies. This creates a significant barrier to widespread deployment of any one particular system modeling and analytics solution, because new adopters with existing modeling and analytics solutions would be required to invest a significant amount of money in order to switch to a new system. Conventional microgrid management systems create “islands” of data that are isolated from other systems. As a result, the overall growth and adoption of microgrid management systems is inhibited.